code: def test_disable_sklearn_autologging_does_not_revert_with_trainer(
      iris_data, transformers_trainer
  ):
      mlflow.autolog()
      mlflow.sklearn.autolog(disable=True)
  
      exp = mlflow.set_experiment(
          experiment_name="trainer_with_sklearn"
      )
  
      transformers_trainer.train()
  
      last_run = mlflow.last_active_run()
      assert last_run.data.metrics["epoch"] == 1.0
      assert last_run.data.params["model_type"] == "distilbert"
  
      pipe = pipeline(
          task="text-classification",
          model=transformers_trainer.model,
          tokenizer=DistilBertTokenizerFast.from_pretrained(
              "distilbert-base-uncased"
          ),
      )
      preds = pipe(
          [
              "Did you hear that guitar solo? Brilliant!",
              "That band should avoid playing live.",
          ]
      )
      assert len(preds) == 2
      assert all(x["score"] > 0 for x in preds)
  
      # Test that autologging does not log since it is manually disabled above.
      with mlflow.start_run(experiment_id=exp.experiment_id) as run:
          model = sklearn.cluster.KMeans()
          X, y = iris_data
          model.fit(X, y)
  
      # Assert that only the run info is logged
      logged_sklearn_data = mlflow.get_run(run.info.run_id)
  
      assert logged_sklearn_data.data.params == {}
      assert logged_sklearn_data.data.metrics == {}
  
      client = mlflow.MlflowClient()
      runs = client.search_runs([exp.experiment_id])
  
      assert len(runs) == 2
      sklearn_run = [
          x for x in runs if x.info.run_id == run.info.run_id
      ]
      assert sklearn_run[0].info == logged_sklearn_data.info
  
imports: import random
  import numpy
  import optuna
  import pytest
  import sklearn
  import sklearn.cluster
  import torch
  from datasets import load_dataset
  from sentence_transformers.losses import CosineSimilarityLoss
  from setfit import SetFitModel, sample_dataset
  from setfit import Trainer
  from setfit import TrainingArguments
  from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast, Trainer, TrainingArguments, pipeline
  import mlflow
label_desc: Use image node_id 13 for calling a global function with example usage: test_disable_sklearn_autologging_does_not_revert_with_trainer(iris_data, transformers_trainer) without return types